3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China
Bibliographic record
Abstract
The identification of primary geochemical haloes can be used to predict mineral resources in deep-seated orebodies through the delineation of element distributions. The Jiama deposits a typical skarn–porphyry Cu–polymetallic deposit in the Gangdese metallogenic belt of Tibet. The Cu–polymetallic skarn, Cu–Mo hornfels, and Mo ± Cu porphyry mineralization there exhibit superimposed geochemical haloes at depth. Three-dimensional (3D) primary geochemical halo modeling was undertaken for the deposit with the aim of providing geochemical data to describe element distributions in 3D space. An overall geochemical zonation of Zn(Pb) → Au → Cu(Ag) → Mo gained from geochemical cross-sections, together with dip-direction skarn zonation Pb–Zn(Cu) → Cu(Au–Ag–Mo) → Mo(Cu) → Cu–Mo(Au–Ag) and vertical zonation Cu–(Pb–Zn) → Mo–(Cu) → Mo–Cu–(Ag–Au–Pb–Zn) → Mo in the #24 exploration profile, indicates potential mineralization at depth. Integrated geochemical anomalies were extracted by kernel principal component analysis, which has the advantage of accommodating nonlinear data. A maximum-entropy model was constructed for deep mineral resources of uncertainty prediction. Three potential deep mineral targets are proposed on the basis of the obtained geochemical information and background.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".